In unsharp masking, a blurred version of an image is used as a model and deviations from the model, are amplified to produce a sharpened image. Facial caricature which amplifiers deviations of a face image from an idealized model of a mean (average) face, are another example of such processing.
Motion magnification reveals deviations from a model, the direction of time, but does not need to detect the model because the direction of time in a video is readily given. In addition, motion magnification assumes that objects are nearly static (i.e., assumes the appearance over time to be nearly constant). In contrast, the present method amplifies deviations from a general spatial curve detected in a single image or frame. The type and location of this curve depends on the application, and the appearance along it may change dramatically.
Existing methods can reveal and estimate internal non parametric variations within an image, assuming that the image contains recurring patterns, and reveals the deviation from a perfect recurrence of the pattern. These methods estimate an “ideal” image with stronger repetitions, and generate a transformation bringing the input images closer to the ideal image. In contrast, the present method relies on parametric shapes within the image and thus can be applied for images without recurring structures. The present methods parametric approach reveals tiny, nearly invisible deviations, which cannot be estimated.